波士顿住宅街数据模型问题
从网上找的机器学习例题#boston住宅数据读入
from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)
这是输出：
Boston House Prices dataset
===========================
Notes
------
Data Set Characteristics:
:Number of Instances: 506
:Number of Attributes: 13 numeric/categorical predictive
:Median Value (attribute 14) is usually the target
:Attribute Information (in order):
- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centres
- RAD index of accessibility to radial highways
- TAX full-value property-tax rate per $10,000
- PTRATIOpupil-teacher ratio by town
- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
:Missing Attribute Values: None
:Creator: Harrison, D. and Rubinfeld, D.L.
This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980. N.B. Various transformations are used in the table on
pages 244-261 of the latter.
The Boston house-price data has been used in many machine learning papers that address regression
problems.
**References**
- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
- many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)
#用pandas模块的dataframe读入boston住宅街的数据
import pandas as pd
df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
df['MEDV'] = boston.target#目标变量读入
x = df.RM.to_frame()
y = df.MEDV
报错信息：
KeyError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in __getattr__(self, key)
60 try:
---> 61 return self
62 except KeyError:
KeyError: 'feature_naMSE'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-11-1277f379165e> in <module>()
1 import pandas as pd
----> 2 df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
3 df['MEDV'] = boston.target#目标变量读入
4 x = df.RM.to_frame()
5 y = df.MEDV
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in __getattr__(self, key)
61 return self
62 except KeyError:
---> 63 raise AttributeError(key)
64
65 def __setstate__(self, state):
AttributeError: feature_naMSE
新手上路，求大佬指教，另外有什么好的机器学习方法吗
听过一天的自学最终找到了问题所在import pandas as pd
df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
df['MEDV'] = boston.target#目标变量读入
x = df.RM.to_frame()
y = df.MEDV
其中的feature_naMSE应该是key中的feature_name{:10_250:}{:10_250:}{:10_250:}
自学还是有好处的 高大上
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